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ScanLag: High-throughput Quantification of Colony Growth and Lag Time

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

Quantifying the lag time to detect barriers in landscape genetics.

E L Landguth¹, S A Cushman, M K Schwartz

¹University of Montana, Mathematics Building, Missoula, MT, 59812, USAUSDA Forest Service, Rocky Mountain Research Station, 800 E Beckwith Ave., Missoula, MT 59801, USAColorado State University, Biology Department, Fort Collins, CO 80523-1878 USAFlathead Lake Biological Station, Division of Biological Sciences, University of Montana, Polson, MT 59860, USACentro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto (CIBIO-UP), Campus Agrário de Vairão, 4485-661 Vairão, Portugal.

Molecular Ecology

|September 8, 2010

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Landscape genetics reveals new barriers emerge quickly (1-15 generations) using Mantel's r, while old barriers persist longer with limited dispersal. Individual-based methods enhance understanding of gene flow and landscape impacts.

Area of Science:

Population Genetics
Landscape Genetics
Conservation Biology

Background:

Understanding spatial genetic patterns is key to landscape genetics.
Quantifying the temporal dynamics of genetic barriers is essential for interpreting landscape influences on gene flow.

Purpose of the Study:

To determine the time required for new landscape barriers to be detected genetically.
To assess how long genetic signatures of past barriers persist after landscape changes.
To compare the efficacy of individual-based and population-based genetic statistics in detecting barriers under various dispersal scenarios.

Main Methods:

Spatially explicit, individual-based simulations were employed.
Evaluated Mantel's r (using Dps) and FST statistics for barrier detection.

Keywords:

computer simulation connectivity conservation genetics gene flow habitat fragmentation landscape modelling power analysis resistance surfaces spatial analysis

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Simulated diverse movement strategies: nearest neighbor dispersal, long-distance dispersal, and panmixia.

Main Results:

Mantel's r detected new barriers within 1-15 generations.
FST required approximately 200 generations to reach 50% of its maximum, while G'ST performed similarly to Mantel's r.
Barrier removal signals disappeared rapidly (<15 generations) with high dispersal, but historical barriers persisted >100 generations with limited dispersal.

Conclusions:

Individual-based statistics like Mantel's r are sensitive to recent landscape changes, offering rapid detection of new barriers.
Limited dispersal can lead to long-lasting genetic legacies, potentially confounding current landscape impact assessments.
Individual-based landscape genetics effectively measures current landscape feature impacts on genetic structure and connectivity, especially in species with significant dispersal.